CN101368985B - Cognitive electric power meter - Google Patents
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- G—PHYSICS
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- G07F—COIN-FREED OR LIKE APPARATUS
- G07F15/00—Coin-freed apparatus with meter-controlled dispensing of liquid, gas or electricity
- G07F15/003—Coin-freed apparatus with meter-controlled dispensing of liquid, gas or electricity for electricity
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D4/00—Tariff metering apparatus
- G01D4/008—Modifications to installed utility meters to enable remote reading
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R21/00—Arrangements for measuring electric power or power factor
- G01R21/133—Arrangements for measuring electric power or power factor by using digital technique
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D2204/00—Indexing scheme relating to details of tariff-metering apparatus
- G01D2204/10—Analysing; Displaying
- G01D2204/14—Displaying of utility usage with respect to time, e.g. for monitoring evolution of usage or with respect to weather conditions
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- G—PHYSICS
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- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D2204/00—Indexing scheme relating to details of tariff-metering apparatus
- G01D2204/20—Monitoring; Controlling
- G01D2204/24—Identification of individual loads, e.g. by analysing current/voltage waveforms
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02B90/20—Smart grids as enabling technology in buildings sector
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
- Y04S20/30—Smart metering, e.g. specially adapted for remote reading
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract
The invention relates to a cognitive electric power meter. An electric power meter 10 includes an embedded decomposition module 40 that is configured to decompose a power meter signal 12 into constituent loads 14 to segregate and identify energy consumption associated with each individual energy consumption device within a plurality of energy consumption devices coupled to the power meter 10.
Description
Technical field
Present invention relates in general to the power consumption metering field, relate more specifically to have the cognitive electric power meter of embedded intelligence, with measured electric current and voltage signal are decomposed into specific composition energy resource consumption.
Background technology
Along with the lasting rising of electric energy/power cost, the consumer is more in the electric energy plan that is intended to its consumption and considers more to bear.People buy the higher automobile of fuel distance travelled, for example comprise littler and are the blended electric power automobiles.
In order to make people in its dwelling house, use the energy/electric power still less, the bill that need list in detail clearly illustrates the energy cost of its power consumption and each equipment thereof.Use the data of listing in detail; The consumer can take measures to practice thrift; Perhaps through the more equipment of high energy efficiency (conditioner, washer/dryer, hot tub, baking box, lighting device etc.) is installed; Perhaps, perhaps when not using, close load simply in the energy/electric power price its power consumption pattern of area change along with one day time variation.Problem is that people do not hope to bear at its each equipment and the required a considerable amount of expenses of installation power sensor on electric loading.
The technology that a kind of power signal that will introduce the power meter measurement is decomposed into the individual load of its composition is called the single-point use side energy and decomposes (SPEED
TM), can obtain from the Enetics company in New York.SPEED
TMProduct comprises record house load data; Be delivered to master station through phone, walk-up, perhaps alternative communication mode then; Master station is treated to individual load interval data with recorder data, takes on energy consumption data that server and data base administration person be used for pre-service and aftertreatment, temperature data, from the inquiry of analysis station and from the inquiry of other infosystems.This known technical operation are on
operating system.
Though known decomposition technique has successfully improved the service quality about consumer's energy consumption; Still there are needs to comprehensive more electric power meter; It does not need master station and/or additional popular resource, is the individual load of its composition with the electric power meter signal decomposition.
Consider previous reasons; Provide a kind of like this electric power meter be useful also be favourable; It utilizes embedded intelligence to be decomposed into the individual load of its composition and to provide power consumption to gather to the user at the power signal of introducing the measurement of ammeter place, and does not have the on-the-spot installation cost of premises.
Summary of the invention
Embodiments of the invention are involved in a kind of cognitive electric power meter and method; Be used for to be decomposed at the input power signal that input place of electric power meter is measured the individual load of its composition; And the on-the-spot installation cost of premises does not take place, allowing provides detailed power consumption to gather to the user.An embodiment relates to a kind of electric power meter, and it comprises:
At least one sensor is used to measure at least one energy needed that is associated with a plurality of energy-dissipating devices and consumes variable, and produces at least one output signal; And
Decomposing module is used for said at least one output signal decomposition individual load for forming, and discerns the energy consumption corresponding to each energy-dissipating device in said a plurality of energy-dissipating devices in view of the above.
An alternative embodiment of the invention relates to the method for a kind of analyst with the power meter signal, and this method comprises:
Measure family expenses ammeter power transmission line signal;
In said family expenses power meter with of the individual load of said power transmission line signal decomposition for forming; With
In said family expenses power meter identification corresponding to a plurality of loads in the energy consumption of each individual load, thereby said a plurality of load is worked together and is produced said family expenses ammeter power transmission line signal.
Another embodiment of the present invention relates to a kind of electric power meter, and it comprises embedded decomposing module, and this module is used for power signal is decomposed into the energy consumption that the composition load is associated with separation and each individual energy-dissipating device of identification and a plurality of energy-dissipating devices.
Description of drawings
When reading following detailed and during with reference to accompanying drawing, can understand these and other characteristic of the present invention, aspect and advantage better, the similar part of similar sign representative in institute's drawings attached, wherein:
Fig. 1 is the synoptic diagram according to an embodiment, illustrates the use of cognitive electric power meter, and its embedded intelligence that has is its a plurality of compositions with a sensing lead signal decomposition;
It is single that Fig. 2 illustrates the current typical electricity charge;
It is single that Fig. 3 illustrates the electricity charge of listing in detail according to the future of an embodiment;
Fig. 4 is the process flow diagram according to an embodiment, illustrates a kind of embedded system and method, is used for the sensing lead signal decomposition is its a plurality of compositions;
Fig. 5 is the process flow diagram according to an embodiment, illustrates cognitive formula operation cycle; And
Fig. 6 is the process flow diagram according to an embodiment, illustrates a kind of decomposition algorithm, is suitable for being provided for the embedded intelligence of cognitive electric power meter shown in Figure 1.
Like what mention in discussing,, also be envisioned that other embodiment of the present invention though top definite figure has been set forth substituting embodiment.In all cases, the disclosure mode non-limiting with expression explained embodiments of the invention.Those skilled in the art can design many other modification and embodiment, and these all fall into the essence and the scope of the principle of the invention.
Embodiment
Fig. 1 is the synoptic diagram according to an embodiment, illustrates the use of cognitive electric power meter 10, and it has the embedded intelligence 40 that a sensing lead signal 12 is resolved into its a plurality of compositions 14.Cognitive electric power meter 10 uses based on modular embedded intelligence, a plurality of individual load that the power signal that will measure at input ammeter 10 places resolves into its composition, and provide to consume to the user and gather, and do not have the on-the-spot installation cost of premises.In one embodiment, the function of cognitive electric power meter is built in the household electrical power meter 10.
Cognitive electric power meter 10 allows the public power supplier to the user detailed electricity charge list to be provided, and shows the utilization rate of whole individual loads, and intrusive mood and expensive sensor need be installed in each circuit load branch.This every month for the user provide first level and be continual energy budget.This will help electricity charge flower that the user understands them where, and can order about saving, safeguards or the generation of upgrading decision.
According to as following that an embodiment will describe in further detail; Cognitive electric power meter 10 is collected mass data, is converted these mass data into a spot of data message; And these information are delivered to bigger infosystem, with provide application and concerning a large number of users upgradeable system.In one embodiment, ammeter 10 constitutes the part of intelligent bill system and is integrated in the electric energy meter.
Cognitive electric power meter 10 can comprise the data fusion from a plurality of different sensors; For example time, date, temperature, security system, TV, computer network etc. need not carry out the on-site training of parameter for the result who produces hope with the load definition that enhancing is provided.In one embodiment, electric power meter 10 communicates for and smart machine direct through PLC, wireless connections or other suitable communication modes.
Fig. 2 illustrates current typical demand charge list 20; And Fig. 3 illustrates the detailed row note electricity charge single 30 when using the cognitive electric power meter 10 with embedded intelligence 40 according to embodiment.Typical bill 20 has only shown the difference between the gauging table reading when beginning in month and finishing, and to calculate total power consumption, provides then and last one year of the comparison of same time bill mutually.Row cradit note 30 provides the target of and Ministry of Energy average to comparison, the whole nation of each common in popular family estimation with electric loading, local equal user's same period in detail.This bill 30 can be adjusted as the energy of first level, and it will make the user to make better decision for the new technology more efficiently of investment.
This in detail electricity charge single 30 of row note can also comprise, the for example suggestion how to save money of user, example be if the user have continuous working the swimming pool pump on user's bill suggestion timer is installed.In addition, should programme to timer makes pond pump move in off-peak period, thereby the user will benefit from the lower cost of electric energy.The user will use less electric energy and in aforementioned sight, buy these electric energy with minimum possible price.Public sector also will be benefited through the mode that shields nonessential load in the demand peak period.
Cognitive electric power meter 10 can be four parts by logical breakdown, has shown the system and method according to an embodiment as shown in Figure 4, is used for the load signal of having measured is decomposed into its a plurality of compositions.This system comprises: 1) the input data read 41,42,43; 2) cognitive formula decomposition algorithm 44; 3) estimated individual load data/information 46; And 4) communication interface 48.
Import/read: in one embodiment, the input data are made up of measured voltage and the electric current from A phase, B phase and the neutral line, shown in square frame 41.A and B phase voltage can be measured to B to the neutral line and A to the neutral line, B according to A.Measure in case accomplish voltage and current, can calculate harmonic wave, power factor, derivative and other synthetic instruments and it is used as the input of cognitive formula decomposition algorithm.
Shown in square frame 42, except voltage and current is measured, also can use the indoor and outdoors temperature in one embodiment.This temperature survey can directly be obtained from local sensor, perhaps can be sent to gauging table with digital form, for example through radio, electric wire or IP network.Temperature data can be from family expenses HVAC system, wired TV, website or other sources.This temperature can be used so that estimate more accurately for example heating and refrigeration load or pond pump load by cognitive formula decomposition algorithm.
Shown in square frame 43, also can be with the input of time and date as cognitive formula decomposition algorithm.This information also can comprise radio, electric wire, IP network or other modes from multiple source.The date and time data can be used for helping to reduce error and simplify cognitive formula decomposition algorithm.
Cognitive formula algorithm also can receive the input data from the wired TV system of indication TV activity and the computer network of instruct computer activity, shown in square frame 43.
Shown in square frame 43, acoustic sensor and with the data that are connected of domestic safety system also can be as the input of cognitive formula algorithm.
Decomposition algorithm/cognition: cognitive formula electronic system can be used to accumulate in large-scale corporation's level, the system-level and device grade intelligence that distributes.The intelligence electric power meter is an example of device level intelligence system, and it can back-up system level interface (for example micro power network) and large-scale corporation's level interface (distribution network).
Continuation is imported the electric energy metrical part that reading section 41,42,43 and decomposing module part 44, individual load data part 46 and communication interface part 48 constitute electric power meter 10 together in one embodiment with reference to figure 4.In one embodiment, individual load data is handled in square frame 46, with washing, artificial atmosphere with wash that dish is movable to interrelate with the data that produced based on decomposition algorithm 44 time of using of fixing a price.Electric energy metrical part can also be used for going out the equipment that used electric energy exceeds national average power utilization rate based on the data identification that decomposition algorithm 400 is produced, and/or be used for utilizing electric power meter to identify the cloth line defect of premises based on the data that decomposing module algorithm 44 is produced.
Fig. 5 is the process flow diagram according to an embodiment, illustrates cognitive formula operation cycle 50.The specific part 51,53,55,57,59 of cognitive formula circulation 50 is in one embodiment based on following principle shown in Fig. 5:
Observe part 51: the voltage and current in the cognitive electric power meter 10 sensing power transmission lines, target are the instantaneous states of confirming power transmission line.This comprises power termination marker detection and preliminary classification.The result is a stack features, data and metadata, and it describes the current state of power transmission line.
Demarcate part (orient element) 53: use previous knowledge then, on more high-grade, further analyze the electric network state observations by ammeter 10 accumulation.This is called the electrical network scene analysis, comprises the for example individuality of the power consumption system of housed device and the actual identification of combination load mark.Interested data can be analyzed on a plurality of time grades according to instantaneous numerical value, with the Kai Heguan of identification load, on hour, day, month and the grade in year, observe the circulation and the trend of long period grade.For example, wash cycle possibly need a plurality of hours to observe the changing pattern of laundry behavior in a week.
Plan part 55: in one embodiment, dynamically construct and bring in constant renewal in the Bayesian model of electrical network, to obtain the target that is predicted as about the electrical network behavior.For example; If started washing machine; Under the situation of given knowledge according to previous knowledge of observing this Machine Type that obtains and/or its operation, can construct the dynamic forecast model of point-device power transmission line load for the operation duration of this device.
Decision section 57: use Bayesian inference and probabilistic to infer in this stage, make judgement about family expenses power transmission line state.It is unusual or excessive load that simple judgement can relate to the metadata token that is associated with specific power consumption device.More complex judging can relate to the suggestion house-owner from cost-effective reason adjustment power setting or running time.In addition, under the favorable condition of user, can start some and judge support control function (preferably the control strategy of AC unit, water heater etc., it is based on user behavior pattern).
Operating part 59: the final stage of cognition circulation 50 relates to the final operation of cognitive electric power meter 10, and its scope comprises that for example generation comprises the report of power utilization rate statistics, the specific part of reporting is labeled as unusually or the ACTIVE CONTROL of excessive loads, the improved suggestion of generation power utilization rate or even housed device.
The characteristic of cognitive circulation 50 is electric power meter 10 stored knowledges and study, continues to adapt to the ability that domestic environment changes, and the ability of improving power monitoring and control.According to an embodiment, the unchangeability of expression and Combined Treatment is the key mechanism of the study in the cognitive electric power meter 10.Knowledge accumulation in the electric power meter 10 is in multidimensional associated array W, and it is cut apart based on the cognitive round-robin specific part of being supported.Each stage 51,53,55,57,59 of circulation 50 has the unique portion of associated array W for its distribution.Said knowledge is by one group of weight w
iExpression, each weight w
iSupport i the stage of circulation 50.These weights are utilized intensified learning mechanism continuous renewal, and said intensified learning mechanism has the cost function Q that represents the family expenses total power consumption.It is to propose the power consumption strategy that cognitive electric power meter 10 is made as a whole final goal; Thereby through the weight of strengthening associated array W along with time integral knowledge, thereby the total power consumption of family expenses is minimized.Each stage of cognitive circulation 50 both can have been assembled also and can have been extracted knowledge from associated array.Gathering can appear in the part that array W distributes to the corresponding cognitive cycle stage 51,53,55,57,59.Yet the extraction of knowledge can be accomplished according to whole associated array W by circulate 50 any stage of cognition.
For example can the sorter 60 based on Bayesian inference be used for decomposition algorithm.According to an embodiment, the basic procedure of algorithm is shown in the process flow diagram of Fig. 6, and the decomposition algorithm 60 that is suitable for providing embedded intelligence that it illustrates according to an embodiment is used for cognitive electric power meter shown in Figure 1 10.
For example, suppose A
0, A
1..., A
NRepresent one group of common housed device.Through carrying out the laboratory experiment of off-line, can obtain conditional probability Pr (TI for all m and n
m/ A
n), promptly open and close device A n and produce instantaneous pattern TI
mPossibility, and conditional probability is programmed in the intelligent meter 10 in advance.
In case observe instantaneous pattern TI; Bayes classifier 66 selects to have maximum joint probability Pr (TI; An) equipment, it is to make posterior probability Pr (An|TI) maximization in essence, it is the possibility of An that posterior probability has been described the equipment that produces viewed instantaneous pattern TI.
For (TI An)=Pr (An) Pr (TI/An) 68, only need obtain Pr (An), because Pr (TI/An) can obtain according to the off-line experiment for classifier calculated joint probability Pr.According to an embodiment, can rely on time series analysis 69 to obtain the approximate of Pr (An).Basic thought is, opens and closes the time point historical series of incident and sensor information on every side through checking about each individual device, so that obtain the probability assignments of next event time for each equipment.Suppose that incident (instantaneous pattern) occurs, (according to aforementioned probability assignments) can obtain the probability density PD (An) of this occurrence time for each equipment.Calculate Pr (An) as probability density weight PD (An)/(PD (A
0)+PD (A
1)+...+PD (A
N)).
This algorithm 60 comes down to two-layer reasoning refinement; Wherein ground floor relies on time series analysis 66 to infer prior probability when instantaneous pattern occurs, and the second layer is for using details that deduction is improved via the instantaneous pattern of instantaneous detector 62 and instantaneous index 64.
Embedded system: in one embodiment, flush bonding module 40 (shown in Fig. 1 and 3) comprises following subsystem: 1) sensor comprises the fundamental voltage sensing and based on the current sense of voltage parallel connection (voltage-shunt) or Hall effect; Additional ambient sensors comprises capacitive character, resistive and based on the temperature and humidity sensor of thermal conductivity, and the microphone that is used for the acoustics sensing.These sensors generally can commercially obtain, and voltage interface is that the peripheral circuit that is easy to obtain or can use minimum obtains; 2) sensor adjustment comprises the filtration and other required pre-service of transducing signal.If the sampling of analog to digital converter (ADC) can guarantee suitable nyquist frequency, then this can use Realization of Analog Circuit or can realize through digital processing; 3) signal sampling and processing, it can comprise general ADC, special IC (ASIC) and digital signal processor (DSP).This sub-systems is calculated the required necessity of decomposition algorithm and is imported/read information; 4) general processor is responsible for system management, time maintenance, record/storage and interface.Decomposition algorithm can be realized on DSP or general processor.Subsystem 3) and 4) can make up and be embodied as single DSP or be structured in the processor in the ADC; And 5) communication transceiver is used to receive user command/inquiry and sends decomposition result and other useful informations.Wired and the wireless communication technology are extensively obtainable, include but not limited to RS232/USB/Firewire, Ethernet, Zigbee, Wifi, bluetooth, RFID, Wireless USB, honeycomb fashion/WMAN etc.
In alternate embodiment, can use single FPGA to realize that whole numerals obtain function and cognitive formula consume analysis algorithm.Soft processor among the FPGA can be used to handle conventional communication and data acquisition task, and the Scouting hardware approach can be used for uncertainty consumption algorithm.Scouting is a kind of high speed technology, is used for using a plurality of hardware copies that are easy to synthesize at FPGA hardware to solve np complete problem.This technology can provide the cost effective method of realizing searching algorithm required in the electric power meter 10, and does not take the Bayes who on expensive more DSP hardware, carries out to calculate.
In addition, processing speed can be adjusted through controlling the quantity of synthesizing the Scout in FPGA.Thereby may be provided in the good compromise between this and the performance.The power division problem is the distortion of knapsack problem (knapsack problem), and known np complete problem has been illustrated the technical solution that is vulnerable to based on Scouting.
Individual load data: in one embodiment; Individual load data (46 among Fig. 4) is formatted as the histogram container, has title for example conditioner, heating arrangement, washing machine, dryer, pond pump, hot tub, light fixture, consumer electronics (clock, radio, video game machine etc.), televisor, baking box, electric furnace, refrigerator, computing machine, printer, dehumidifier, coffee pot, hair dryer and curler etc.According to an embodiment, each histogram container will comprise the consumption corresponding to measured time cycle, peak load, average load, and the probability metrics of correctness.
Output/information communication: in one embodiment, output will be read then by public sector and will be combined in the subscriber's account from the information of cognitive electric power meter (CEPM) 10.CEPM 10 comprises than the common more information that comprises in the contemporary electricity charge list, and it is read by automatic reader or manual metering person usually.In one embodiment, the output of CEPM 10 will have standard format, will comprise the 8-10 bit digital more information than the present age.In one embodiment, coding strategy can use 8-10 ascii character and be nonnumeric, so that manual work is read.Said ascii character can be expressed as sexadecimal or other codes, so that more information is compressed in the same 8-10 character.The quantity of character will become inessential in future, because the manual work of ammeter is read being substituted by the bilateral network signal post between gauging table and the public sector.
Information in the gauging table message for example can comprise that the moon of conditioner, well heater, coffee pot, refrigerator, hot tub, swimming pool, lighting device, clock, computing machine, baking box, cooking stove, hair dryer, curling tongs, televisor, video game machine, electric heater, exercise equipment etc. consumes.This information can be in office's grade accumulation similarly to compare between neighbours, to search unusually.For example, if there are ten contiguous houses, wherein nine houses have the AC cost of every Yue $30, and house has the cost of every Yue $200, and then the user of Ju You $200 cost should check its AC unit.
In addition, if the user see that it has used and double the average energy resource consumption in the whole nation, they should own target setting energy savings to become better terrestrial.
Subscriber's account also can comprise from last month and last one year data mutually of the same period.This will help to discern the trend of energy consumption and wearing out of equipment.For example, if refrigerator or other equipment do not have appropriate maintenance will use significantly more electric power.
In addition, except the common interface of standard, the user can hope to have local wired or wireless interface, and the application program through the web browser type conducts interviews to its cognitive electric power meter.
Sum up explanation, cognitive electric power meter has embedded intelligence and is decomposed into its a plurality of compositions with the load signal that has been measured.Said cognitive electric power meter uses the power signal based on the embedded intelligence input electric power table measurement of module to be decomposed into the individual load of its composition and to provide consumption to gather to the user, and does not have the on-the-spot installation cost of premises.According to an embodiment, the function of said cognitive electric power meter is implemented in the household electrical power meter.
Though only explain and described specific embodiment of the present invention here, those skilled in the art will expect a lot of the modification and variation.Thereby should be appreciated that appended claim intention covering falls into all such modifications and variation in the spirit of the present invention.
Claims (77)
1. electric power meter, it comprises:
At least one sensor, it is configured to measure at least one energy needed that is associated with a plurality of energy-dissipating devices and consumes variable, and produces at least one output signal from it; And
Decomposing module, it is configured to said at least one output signal decomposition individual load for forming, and discerns the energy consumption corresponding to each energy-dissipating device in said a plurality of energy-dissipating devices in view of the above,
Wherein said decomposing module comprises cognitive formula decomposition algorithm, and this algorithm is configured to stored knowledge and study, adapts to the variation of family expenses power transmission line characteristic continuously, improving monitoring of family expenses power transmission line and energy-dissipating device control ability, and further
Wherein said decomposition algorithm adopts constant expression and Combined Treatment to realize its study.
2. electric power meter according to claim 1, wherein said at least one sensor is selected from current sensor, voltage sensor, temperature sensor and acoustic sensor.
3. electric power meter according to claim 1 also comprises communication interface, and it is arranged to and receives user command and inquiry, and is used to send decomposition result.
4. electric power meter according to claim 3, wherein said communication interface is selected from wired and wireless communication technology.
5. electric power meter according to claim 3, wherein said communication interface are selected from RSb232, USB, Firewire, Ethernet, Zigbee, Wifi, bluetooth, RFID, Wireless USB, honeycomb fashion and the WMAN communication technology.
6. electric power meter according to claim 1; Wherein said decomposing module further be configured to carry out cognitive formula circulation with: a) confirm the instantaneous state of family expenses power transmission line; B) carry out the electrical network scene analysis is coupled to the energy-dissipating device of said family expenses power transmission line with identification individuality and combination load mark based on said instantaneous state; C) the following electrical network behavior of individuality with the combination energy-dissipating device of said family expenses power transmission line is coupled in prediction; D) produce judgement based on said following electrical network behavior about said family expenses power transmission line state, and e) energy needed preservation action carried out based on said judgement.
7. electric power meter according to claim 1, wherein said decomposition algorithm uses the sorter based on Bayesian inference, and this sorter is configured to discern the energy-dissipating device on the family expenses power transmission line.
8. electric power meter according to claim 1 also comprises communication interface, and it is configured to through PLC or wireless connections are direct and smart machine communicates.
9. electric power meter according to claim 1, wherein said decomposing module are integrated in the electric energy metrical part of said electric power meter, to form the part of the intelligent accounting system in the said electric power meter.
10. electric power meter according to claim 9, wherein said electric energy metrical partly are configured to laundry, artificial atmosphere and wash the movable time correlation with the use of fixing a price based on the data that produced by said decomposing module of dish join.
11. electric power meter according to claim 9, wherein said electric energy metrical partly are configured to discern a kind of like this electrical equipment based on the data that produced by said decomposing module, the energy that this electrical equipment uses is more than the national average energy consumption of said electrical equipment.
12. electric power meter according to claim 9, wherein said electric energy metrical partly are configured to discern based on the data that produced by said decomposing module the cloth line defect of the premises that uses said electric power meter.
13. an electric power meter, it comprises:
At least one sensor, it is configured to measure at least one energy needed that is associated with a plurality of energy-dissipating devices and consumes variable, and produces at least one output signal from it; And
Decomposing module, it is configured to said at least one output signal decomposition individual load for forming, and discerns the energy consumption corresponding to each energy-dissipating device in said a plurality of energy-dissipating devices in view of the above,
Wherein said decomposing module comprises cognitive formula decomposition algorithm, and this algorithm is configured to stored knowledge and study, adapts to the variation of family expenses power transmission line characteristic continuously, improving monitoring of family expenses power transmission line and energy-dissipating device control ability, and further
Wherein said decomposition algorithm accumulates in the multidimensional associated array, said array based on the cognitive formula round-robin specific part of its support by segmentation.
14. electric power meter according to claim 13, wherein said at least one sensor is selected from current sensor, voltage sensor, temperature sensor and acoustic sensor.
15. electric power meter according to claim 13 also comprises communication interface, it is arranged to and receives user command and inquiry, and is used to send decomposition result.
16. electric power meter according to claim 15, wherein said communication interface is selected from wired and wireless communication technology.
17. electric power meter according to claim 15, wherein said communication interface are selected from RSb232, USB, Firewire, Ethernet, Zigbee, Wifi, bluetooth, RFID, Wireless USB, honeycomb fashion and the WMAN communication technology.
18. electric power meter according to claim 13; Wherein said decomposing module further be configured to carry out cognitive formula circulation with: a) confirm the instantaneous state of family expenses power transmission line; B) carry out the electrical network scene analysis is coupled to the energy-dissipating device of said family expenses power transmission line with identification individuality and combination load mark based on said instantaneous state; C) individuality of said family expenses power transmission line and the following electrical network behavior of combination power consumption are coupled in prediction; D) produce judgement based on said following electrical network behavior about said family expenses power transmission line state, and e) energy needed preservation action carried out based on said judgement.
19. electric power meter according to claim 13, wherein said decomposition algorithm uses the sorter based on Bayesian inference, and this sorter is configured to discern the energy-dissipating device on the family expenses power transmission line.
20. an electric power meter, it comprises:
At least one sensor, it is configured to measure at least one energy needed that is associated with a plurality of energy-dissipating devices and consumes variable, and produces at least one output signal from it; And
Decomposing module; It is configured to said at least one output signal decomposition individual load for forming; And discern energy consumption in view of the above corresponding to each energy-dissipating device in said a plurality of energy-dissipating devices; Wherein said decomposing module comprises cognitive formula decomposition algorithm, and this algorithm is configured to the energy-dissipating device on identification family expenses power transmission line under the situation that does not need on-site training.
21. electric power meter according to claim 20, wherein said at least one sensor is selected from current sensor, voltage sensor, temperature sensor and acoustic sensor.
22. electric power meter according to claim 20 also comprises communication interface, it is arranged to and receives user command and inquiry, and is used to send decomposition result.
23. electric power meter according to claim 22, wherein said communication interface is selected from wired and wireless communication technology.
24. electric power meter according to claim 22, wherein said communication interface are selected from RS232, USB, Firewire, Ethernet, Zigbee, Wifi, bluetooth, RFID, Wireless USB, honeycomb fashion and the WMAN communication technology.
25. electric power meter according to claim 20; Wherein said decomposing module further be configured to carry out cognitive formula circulation with: a) confirm the instantaneous state of family expenses power transmission line; B) carry out the electrical network scene analysis is coupled to the energy-dissipating device of said family expenses power transmission line with identification individuality and combination load mark based on said instantaneous state; C) the following electrical network behavior of individuality with the combination energy-dissipating device of said family expenses power transmission line is coupled in prediction; D) produce judgement based on said following electrical network behavior about said family expenses power transmission line state, and e) energy needed preservation action carried out based on said judgement.
26. electric power meter according to claim 20, wherein said decomposition algorithm uses the sorter based on Bayesian inference, and this sorter is configured to discern the energy-dissipating device on the family expenses power transmission line.
27. an analyst is with the method for power meter signal, this method comprises:
Measure family expenses power meter power transmission line signal;
In said family expenses power meter with of the individual load of said power transmission line signal decomposition for forming; With
In said family expenses power meter identification corresponding to a plurality of loads in the energy consumption of each individual load, thereby said a plurality of load is worked together and is produced said family expenses power meter power transmission line signal;
Based on said measurement, decomposition and identification, stored knowledge and study, and the variation that adapts to family expenses power transmission line characteristic continuously, to improve monitoring of family expenses power transmission line and energy-dissipating device control ability, wherein said study is based on constant expression and Combined Treatment technology.
28. method according to claim 27 wherein comprises said power transmission line signal decomposition for the individual load of forming:
Confirm the instantaneous state of family expenses power transmission line, and carry out the electrical network scene analysis is coupled to the energy-dissipating device of said family expenses power transmission line with identification individuality and combination load mark based on said instantaneous state.
29. method according to claim 27, the energy consumption of wherein discerning corresponding to each individual load in a plurality of loads comprises:
The following electrical network behavior of individuality with the combination energy-dissipating device of said family expenses power transmission line is coupled in prediction;
Produce judgement based on said following electrical network behavior about said family expenses power transmission line state, and
Carry out energy needed based on said judgement and preserve action.
30. method according to claim 27, wherein said measurement, decompose and identification comprise configuration based on the sorter of Bayesian inference with the energy-dissipating device on the identification family expenses power transmission line.
31. method according to claim 27 also comprises through PLC or wireless connections are direct and smart machine communicates.
32. method according to claim 27 also is included in the energy consumption of the interior identification of electric energy metrical part of said family expenses power meter corresponding to each individual load in a plurality of loads, to form the part of the intelligent accounting system in the said family expenses power meter.
33. method according to claim 32 also comprises laundry, artificial atmosphere and washes that dish is movable to be joined with the data that partly produce based on the electric energy metrical by the said family expenses power meter time correlation of using of fixing a price.
34. method according to claim 32 comprises that also the data that partly produce based on the electric energy metrical by said family expenses power meter discern a kind of like this electrical equipment, the energy that this electrical equipment uses is more than the national average energy consumption of said electrical equipment.
35. method according to claim 32 comprises that also the data that partly produce based on the electric energy metrical by said family expenses power meter discern the cloth line defect of the premises that uses the family expenses power meter.
36. an analyst is with the method for power meter signal, this method comprises:
Measure family expenses power meter power transmission line signal;
In said family expenses power meter with of the individual load of said power transmission line signal decomposition for forming; With
In said family expenses power meter identification corresponding to a plurality of loads in the energy consumption of each individual load, thereby said a plurality of load is worked together and is produced said family expenses power meter power transmission line signal; And
Based on said measurement, decomposition and identification; Stored knowledge and study; And the variation that adapts to family expenses power transmission line characteristic continuously; To improve monitoring of family expenses power transmission line and energy-dissipating device control ability, wherein stored knowledge comprises and assembles the multidimensional associated array, said array based on the cognitive formula round-robin specific part of expecting by segmentation.
37. method according to claim 36 wherein comprises said power transmission line signal decomposition for the individual load of forming:
Confirm the instantaneous state of family expenses power transmission line, and carry out the electrical network scene analysis is coupled to the energy-dissipating device of said family expenses power transmission line with identification individuality and combination load mark based on said instantaneous state.
38. method according to claim 36, the energy consumption of wherein discerning corresponding to each individual load in a plurality of loads comprises:
The following electrical network behavior of individuality with the combination energy-dissipating device of said family expenses power transmission line is coupled in prediction;
Produce judgement based on said following electrical network behavior about said family expenses power transmission line state, and
Carry out energy needed based on said judgement and preserve action.
39. method according to claim 36, wherein said measurement, decompose and identification comprise configuration based on the sorter of Bayesian inference with the energy-dissipating device on the identification family expenses power transmission line.
40. method according to claim 36 also comprises through PLC or wireless connections are direct and smart machine communicates.
41. method according to claim 36 also is included in the energy consumption of the interior identification of electric energy metrical part of said family expenses power meter corresponding to each individual load in a plurality of loads, to form the part of the intelligent accounting system in the said family expenses power meter.
42., also comprise laundry, artificial atmosphere and wash that dish is movable to be joined with the data that partly produce based on the electric energy metrical by the said family expenses power meter time correlation of using of fixing a price according to the described method of claim 41.
43. according to the described method of claim 41, comprise that also the data that partly produce based on the electric energy metrical by said family expenses power meter discern a kind of like this electrical equipment, the energy that this electrical equipment uses is more than the national average energy consumption of said electrical equipment.
44., comprise that also the data that partly produce based on the electric energy metrical by said family expenses power meter discern the cloth line defect of the premises that uses the family expenses power meter according to the described method of claim 41.
45. an analyst is with the method for power meter signal, this method comprises:
Measure family expenses power meter power transmission line signal;
In said family expenses power meter with of the individual load of said power transmission line signal decomposition for forming; With
In said family expenses power meter identification corresponding to a plurality of loads in the energy consumption of each individual load, thereby said a plurality of load is worked together and is produced said family expenses power meter power transmission line signal; Wherein said identification is included in the energy-dissipating device on the identification family expenses power transmission line under the situation that does not need on-site training.
46., wherein said power transmission line signal decomposition is comprised for the individual load of forming according to the described method of claim 45:
Confirm the instantaneous state of family expenses power transmission line, and carry out the electrical network scene analysis is coupled to the energy-dissipating device of said family expenses power transmission line with identification individuality and combination load mark based on said instantaneous state.
47. according to the described method of claim 45, the energy consumption of wherein discerning corresponding to each individual load in a plurality of loads comprises:
The following electrical network behavior of individuality with the combination energy-dissipating device of said family expenses power transmission line is coupled in prediction;
Produce judgement based on said following electrical network behavior about said family expenses power transmission line state, and
Carry out energy needed based on said judgement and preserve action.
48. according to the described method of claim 45, wherein said measurement, decomposition and identification comprise configuration based on the sorter of Bayesian inference with the energy-dissipating device on the identification family expenses power transmission line.
49. according to the described method of claim 45, also comprise through PLC or wireless connections directly and smart machine communicate.
50., also be included in the energy consumption of the interior identification of electric energy metrical part of said family expenses power meter, to form the part of the intelligent accounting system in the said family expenses power meter corresponding to each individual load in a plurality of loads according to the described method of claim 45.
51., also comprise laundry, artificial atmosphere and wash that dish is movable to be joined with the data that partly produce based on the electric energy metrical by the said family expenses power meter time correlation of using of fixing a price according to the described method of claim 50.
52. according to the described method of claim 50, comprise that also the data that partly produce based on the electric energy metrical by said family expenses power meter discern a kind of like this electrical equipment, the energy that this electrical equipment uses is more than the national average energy consumption of said electrical equipment.
53., comprise that also the data that partly produce based on the electric energy metrical by said family expenses power meter discern the cloth line defect of the premises that uses the family expenses power meter according to the described method of claim 50.
54. electric power meter; It comprises embedded decomposing module; This module is arranged to power signal is decomposed into the energy consumption that a plurality of composition loads are associated with each individual energy-dissipating device in a plurality of energy-dissipating devices with separation and identification; Wherein said decomposing module comprises cognitive formula decomposition algorithm; This algorithm is configured to stored knowledge and study, adapts to the variation of family expenses power transmission line characteristic continuously, and to improve monitoring of family expenses power transmission line and energy-dissipating device control ability, wherein said decomposition algorithm comprises that constant expression and Combined Treatment realize its study.
55. according to the described electric power meter of claim 54; Wherein said cognitive formula decomposition algorithm be configured to carry out cognitive formula circulation with: a) confirm the instantaneous state of family expenses power transmission line; B) carry out the electrical network scene analysis is coupled to the energy-dissipating device of said family expenses power transmission line with identification individuality and combination load mark based on said instantaneous state; C) the following electrical network behavior of individuality with the combination energy-dissipating device of said family expenses power transmission line is coupled in prediction; D) produce judgement based on said following electrical network behavior about said family expenses power transmission line state, and e) energy needed preservation action carried out based on said judgement.
56. according to the described electric power meter of claim 54, wherein said cognitive formula decomposition algorithm uses the sorter based on Bayesian inference, this sorter is configured to discern the energy-dissipating device on the family expenses power transmission line.
57. according to the described electric power meter of claim 54, also comprise communication interface, it is configured to through PLC or wireless connections are direct and smart machine communicates.
58. according to the described electric power meter of claim 54, wherein said decomposing module is integrated in the electric energy metrical part of said electric power meter, to form the part of the intelligent accounting system in the said electric power meter.
59. according to the described electric power meter of claim 58, wherein said electric energy metrical partly is configured to laundry, artificial atmosphere and washes the movable time correlation with the use of fixing a price based on the data that produced by said decomposing module of dish join.
60. according to the described electric power meter of claim 58, wherein said electric energy metrical partly is configured to discern a kind of like this electrical equipment based on the data that produced by said decomposing module, the energy that this electrical equipment uses is more than the national average energy consumption of said electrical equipment.
61. according to the described electric power meter of claim 58, wherein said electric energy metrical partly is configured to discern based on the data that produced by said decomposing module the cloth line defect of the premises that uses said electric power meter.
62. electric power meter; It comprises embedded decomposing module; This module is arranged to power signal is decomposed into the energy consumption that a plurality of composition loads are associated with each individual energy-dissipating device in a plurality of energy-dissipating devices with separation and identification; Wherein said decomposing module comprises cognitive formula decomposition algorithm, and this algorithm is configured to stored knowledge and study, adapts to the variation of family expenses power transmission line characteristic continuously, to improve monitoring of family expenses power transmission line and energy-dissipating device control ability; Wherein said decomposition algorithm accumulates in the multidimensional associated array, said array based on the cognitive formula round-robin specific part of its support by segmentation.
63. according to the described electric power meter of claim 62; Wherein said cognitive formula decomposition algorithm be configured to carry out cognitive formula circulation with: a) confirm the instantaneous state of family expenses power transmission line; B) carry out the electrical network scene analysis is coupled to the energy-dissipating device of said family expenses power transmission line with identification individuality and combination load mark based on said instantaneous state; C) the following electrical network behavior of individuality with the combination energy-dissipating device of said family expenses power transmission line is coupled in prediction; D) produce judgement based on said following electrical network behavior about said family expenses power transmission line state, and e) energy needed preservation action carried out based on said judgement.
64. according to the described electric power meter of claim 62, wherein said cognitive formula decomposition algorithm uses the sorter based on Bayesian inference, this sorter is configured to discern the energy-dissipating device on the family expenses power transmission line.
65. according to the described electric power meter of claim 62, also comprise communication interface, it is configured to through PLC or wireless connections are direct and smart machine communicates.
66. according to the described electric power meter of claim 62, wherein said decomposing module is integrated in the electric energy metrical part of said electric power meter, to form the part of the intelligent accounting system in the said electric power meter.
67. according to the described electric power meter of claim 65, wherein said electric energy metrical partly is configured to laundry, artificial atmosphere and washes the movable time correlation with the use of fixing a price based on the data that produced by said decomposing module of dish join.
68. according to the described electric power meter of claim 65, wherein said electric energy metrical partly is configured to discern a kind of like this electrical equipment based on the data that produced by said decomposing module, the energy that this electrical equipment uses is more than the national average energy consumption of said electrical equipment.
69. according to the described electric power meter of claim 65, wherein said electric energy metrical partly is configured to discern based on the data that produced by said decomposing module the cloth line defect of the premises that uses said electric power meter.
70. electric power meter; It comprises embedded decomposing module; This module is arranged to power signal is decomposed into the energy consumption that a plurality of composition loads are associated with each individual energy-dissipating device in a plurality of energy-dissipating devices with separation and identification; Wherein said decomposing module comprises cognitive formula decomposition algorithm, and this algorithm is configured to the energy-dissipating device on identification family expenses power transmission line under the situation that does not need on-site training.
71. according to the described electric power meter of claim 70; Wherein said cognitive formula decomposition algorithm be configured to carry out cognitive formula circulation with: a) confirm the instantaneous state of family expenses power transmission line; B) carry out the electrical network scene analysis is coupled to the energy-dissipating device of said family expenses power transmission line with identification individuality and combination load mark based on said instantaneous state; C) the following electrical network behavior of individuality with the combination energy-dissipating device of said family expenses power transmission line is coupled in prediction; D) produce judgement based on said following electrical network behavior about said family expenses power transmission line state, and e) energy needed preservation action carried out based on said judgement.
72. according to the described electric power meter of claim 70, wherein said cognitive formula decomposition algorithm uses the sorter based on Bayesian inference, this sorter is configured to discern the energy-dissipating device on the family expenses power transmission line.
73. according to the described electric power meter of claim 70, also comprise communication interface, it is configured to through PLC or wireless connections are direct and smart machine communicates.
74. according to the described electric power meter of claim 70, wherein said decomposing module is integrated in the electric energy metrical part of said electric power meter, to form the part of the intelligent accounting system in the said electric power meter.
75. according to the described electric power meter of claim 74, wherein said electric energy metrical partly is configured to laundry, artificial atmosphere and washes the movable time correlation with the use of fixing a price based on the data that produced by said decomposing module of dish join.
76. according to the described electric power meter of claim 74, wherein said electric energy metrical partly is configured to discern a kind of like this electrical equipment based on the data that produced by said decomposing module, the energy that this electrical equipment uses is more than the national average energy consumption of said electrical equipment.
77. according to the described electric power meter of claim 74, wherein said electric energy metrical partly is configured to discern based on the data that produced by said decomposing module the cloth line defect of the premises that uses said electric power meter.
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| US11/893,170 US7693670B2 (en) | 2007-08-14 | 2007-08-14 | Cognitive electric power meter |
| US11/893170 | 2007-08-14 |
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| CN101368985A CN101368985A (en) | 2009-02-18 |
| CN101368985B true CN101368985B (en) | 2012-10-10 |
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| US (1) | US7693670B2 (en) |
| EP (1) | EP2026299A1 (en) |
| JP (1) | JP2009047694A (en) |
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Also Published As
| Publication number | Publication date |
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| EP2026299A1 (en) | 2009-02-18 |
| US20090045804A1 (en) | 2009-02-19 |
| CN101368985A (en) | 2009-02-18 |
| JP2009047694A (en) | 2009-03-05 |
| US7693670B2 (en) | 2010-04-06 |
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